Database storage can be expensive to purchase, maintain, and house. Often times as new business models are integrated into the database design, the data elements that have become redundant or stale are seldom removed and needlessly consume storage in the database. This may be due to factors such as complexity of the database, staff turnover, unfamiliarity with the data model, and uncertainty as to whether data elements can be removed safely without affecting applications or workloads. Current methods to reclaim database storage include multi-temperature database storage in which database systems manage storage by ranking rows, or records, of data based on their use, including factors such as frequency of access, age, volatility, and importance of query performance. Multi-temperature storage systems may store frequently used data into hot storage, or fast and expensive storage, while data not frequently used is stored in cold storage, or slower and less expensive storage. The problem with multi-temperature storage is that while an entire row, or record, may be considered hot data, some data elements of the record may not be used as frequently as the data elements meriting the record designation in hot storage. For example, a Department of Motor Vehicles database may contain information on the registered vehicles of a state. A vehicle record may include hot, or frequently accessed, data such as make and model, but it also may include cold, or less frequently accessed, data such as engine type. Because the record is a single entity, not only is the hot data such as vehicle make and model stored in hot storage, but the cold data such as engine type as well.